https://github.com/abcdef2000/R2RNet
공식 코드R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network
Practical Face Reconstruction via Differentiable Ray Tracing
Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles
Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
Advbox: a toolbox to generate adversarial examples that fool neural networks
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
A new face swap method for image and video domains: a technical report
Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities
Face Verification with Challenging Imposters and Diversified Demographics
GANimation: Anatomically-aware Facial Animation from a Single Image
DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models
It's All in the Head: Representation Knowledge Distillation through Classifier Sharing
Learning from Failure: Training Debiased Classifier from Biased Classifier
ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation
ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis
Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.